Context systems for AI-assisted work.
Decision memory, review pathways, and reusable operating models.
Now
I’m currently working on AI workflow architecture: source-of-truth systems, decision memory, review pathways, and reusable operating models for AI-assisted work.
Before
Work
A working LifeOS system built around source-of-truth architecture, context routing, durable memory, raw evidence, review loops, and system repair.
The work: define what becomes current context, what becomes durable memory, what stays raw evidence, and how repeated AI workflow failures get patched back into the system.
A meeting workflow for turning messy transcripts into confirmed decisions, rationale, risks, assumptions, open questions, and next actions.
The work: separate confirmed decisions from unresolved discussion, capture rationale and risks, and update the source of truth through an ADR-style decision memory structure.
A support knowledge architecture for making AI-assisted answers reliable before automating the support layer.
The work: define canonical answers, edge cases, escalation paths, review boundaries, quality bars, and feedback loops so every support failure improves the knowledge system instead of becoming another one-off fix.
An AI-ready creative workflow that turns brand context, examples, anti-examples, constraints, terminology, prompts, and review criteria into reusable creative context.
The work: build a creative operating system where briefs, outputs, feedback, performance signals, and learnings are captured, codified, shared, and reused — so creative work gets faster, clearer, and more consistent instead of restarting from scratch every time.
Journal